AMINGA 2024 Student Survey Analysis

Setup

Code 
library(here)
library(readr)
library(janitor)
library(dplyr)
library(magrittr)
library(likert)
library(ggplot2)
library(tidyr)

# wordcloud
library(tidytext)
library(wordcloud)
Code 
data_location <- here("Data", "2024 - AMINGA Youth Participant Survey Responses - Master.csv")

survey <- read_csv(data_location, skip = 2) %>% 
  clean_names() 


survey_df <- survey %>% 
  dplyr::select(
    status, 
    gender_genero, 
    sport_overall_experience_1_not_great_3_neutral_5_amazing_experiencia_na_modalidade_1_suficiente_3_bom_5_muito_bom,
    english_1_not_great_3_neutral_5_amazing_ingles_1_suficiente_3_bom_5_muito_bom,
    art_1_not_great_3_neutral_5_amazing_arte_1_suficiente_3_bom_5_muito_bom,
    computer_1_not_great_3_neutral_5_amazing_computer_1_suficiente_3_bom_5_muito_bom,
    team_building_1_not_great_3_neutral_5_amazing_trabalho_de_equipa_1_suficiente_3_bom_5_muito_bom,
    human_rights_direitos_humanos_1_not_great_3_neutral_5_amazing_direitos_humanos_1_suficiente_3_bom_5_muito_bom,
    career_readiness_orientacao_de_carreira_1_not_great_3_neutral_5_amazing_orientacao_de_carreira_1_suficiente_3_bom_5_muito_bom,
    yoga_mindfulness_1_not_great_3_neutral_5_amazing_ioga_atencao_plena_1_suficiente_3_bom_5_muito_bom,
    food_1_not_great_3_neutral_5_amazing_comida_1_suficiente_3_bom_5_muito_bom,
    aminga_staff_2024_overall_1_not_great_3_neutral_5_amazing_equipa_de_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
    aminga_camp_2024_overall_1_not_great_3_neutral_5_amazing_campus_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
    do_you_want_to_return_to_aminga_camp_in_2024_queres_voltar_para_o_campus_aminga_em_2024
) %>% 
  dplyr::rename(
    english = english_1_not_great_3_neutral_5_amazing_ingles_1_suficiente_3_bom_5_muito_bom,
    art = art_1_not_great_3_neutral_5_amazing_arte_1_suficiente_3_bom_5_muito_bom,
    computer = computer_1_not_great_3_neutral_5_amazing_computer_1_suficiente_3_bom_5_muito_bom,
    team_building = team_building_1_not_great_3_neutral_5_amazing_trabalho_de_equipa_1_suficiente_3_bom_5_muito_bom,
    human_rights = human_rights_direitos_humanos_1_not_great_3_neutral_5_amazing_direitos_humanos_1_suficiente_3_bom_5_muito_bom,
    career_readiness = career_readiness_orientacao_de_carreira_1_not_great_3_neutral_5_amazing_orientacao_de_carreira_1_suficiente_3_bom_5_muito_bom,
    yoga = yoga_mindfulness_1_not_great_3_neutral_5_amazing_ioga_atencao_plena_1_suficiente_3_bom_5_muito_bom,
    food = food_1_not_great_3_neutral_5_amazing_comida_1_suficiente_3_bom_5_muito_bom,
    staff_overall = aminga_staff_2024_overall_1_not_great_3_neutral_5_amazing_equipa_de_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
    camp_overall = aminga_camp_2024_overall_1_not_great_3_neutral_5_amazing_campus_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
    sport = status,
    gender = gender_genero,
    sport_experience = sport_overall_experience_1_not_great_3_neutral_5_amazing_experiencia_na_modalidade_1_suficiente_3_bom_5_muito_bom, 
    return_next_year = do_you_want_to_return_to_aminga_camp_in_2024_queres_voltar_para_o_campus_aminga_em_2024
  ) %>% 
  mutate(
    sport = trimws(gsub("\\([^\\(\\)]*\\)", "", sport)),
    gender = trimws(gsub("\\([^\\(\\)]*\\)", "", gender)),
    return_next_year = trimws(gsub("\\s*\\([^\\)]+\\)","",return_next_year))
  ) %>% 
  mutate(across(where(is.numeric), function(x) factor(x, levels = 1:5, labels = c(
              "Not Great", 
              "Meh",
              "Neutral",
              "Great",
              "Amazing"
            ))))  %>% 
  as.data.frame()


comments_sport_df <- survey %>% 
  select(
    i_loved_about_the_sport_eu_amei_sobre_a_modalidade, 
    i_would_change_about_the_sport_eu_mudaria_sobre_a_modalidade,
    comments_regarding_art_class_comentarios_sobre_as_aulas_de_arte,
    comments_regarding_english_class_comentarios_sobre_a_aula_de_ingles,
    comments_regarding_computer_class_comentarios_sobre_as_aulas_de_informatica,
    comments_regarding_team_building_comentarios_sobre_trabalho_de_equipa,
    comments_regarding_human_rights_comentarios_sobre_direitos_humanos,
    comments_regarding_career_readiness_comentarios_sobre_orientacao_de_carreira,
    comments_regarding_yoga_class_comentarios_sobre_a_aula_de_yoga,
    comments_regarding_the_food_comentarios_sobre_a_comida,
    aminga_staff_2024_overall_insert_appreciations_for_any_of_the_staff_equipa_de_aminga_em_geral_algum_comentario_sobre_qualquer_um_da_equipa_aminga,
    any_comments_or_suggestions_algum_comentario_ou_sugestao
) 


prep_word_cloud <- function(colname){
  
  pt_stopwords <- tibble(word = stopwords::stopwords('pt'))
  
  comments_sport_df %>% 
    select({{colname}}) %>% 
    unnest_tokens(word, {{colname}}) %>% 
    count(word, sort = TRUE)%>% 
    anti_join(pt_stopwords, by = "word")
  
}

Sports Overall

Code 
likert_overall_sport <- likert(items = survey_df %>% dplyr::select(sport_experience))

likert_overall_sport
              Item Not Great Meh  Neutral    Great  Amazing
1 sport_experience         0   0 17.94872 11.53846 70.51282

We can see that over 70% of students thought that the camp was amazing.

Code 
plot(likert_overall_sport)

We can see that over 82% of students thought that the camp was either great or amazing.

Subjects Overall

Code 
likert_subjects <- likert(items = survey_df %>% dplyr::select(art, english, computer, human_rights, career_readiness, team_building,   yoga))
Code 
likert_subjects$results
Item Not Great Meh Neutral Great Amazing
art 1.282051 0.000000 19.230769 5.128205 74.35897
english 2.597403 3.896104 33.766234 11.688312 48.05195
computer 2.564103 1.282051 25.641026 10.256410 60.25641
human_rights 0.000000 0.000000 20.779221 11.688312 67.53247
career_readiness 8.860760 5.063291 29.113924 16.455696 40.50633
team_building 1.282051 0.000000 1.282051 1.282051 96.15385
yoga 2.666667 8.000000 10.666667 9.333333 69.33333
Code 
plot(likert_subjects, centered=TRUE, center=3, include.center=TRUE)

We can see that over 97% of students thought that team building was either great or amazing. In contrast, 57% of students thought that career readiness was either great or amazing.

Food Overall

Code 
likert_overall_food <- likert(items = survey_df %>% dplyr::select(food))
Code 
likert_overall_food$results
Item Not Great Meh Neutral Great Amazing
food 0 0 7.792208 3.896104 88.31169

We can see that over 88% of students thought that the food was amazing.

Code 
plot(likert_overall_food)

Staff Overall

Code 
likert_overall_staff <- likert(items = survey_df %>% dplyr::select(staff_overall))
Code 
likert_overall_staff$results
Item Not Great Meh Neutral Great Amazing
staff_overall 0 0 3.846154 7.692308 88.46154
Code 
plot(likert_overall_staff)

We can see that 96% of students thought that the staff was great or amazing. Furthermore, no students had a negative opinion of staff.

Camp Overall

Code 
likert_overall_camp <- likert(items = survey_df %>% dplyr::select(camp_overall))
Code 
likert_overall_camp$results
Item Not Great Meh Neutral Great Amazing
camp_overall 0 0 0 10.25641 89.74359
Code 
plot(likert_overall_camp)

We can see that all of the students had a poitive experience of the camp.

Return 2025?

The question originally was the following:

  • Do you want to return to AMINGA camp in 2024? Queres voltar para o campus Aminga em 2024?
Code 
return_next_year_df <- survey_df %>% 
  dplyr::select(return_next_year) %>% 
  count(return_next_year) %>% 
  mutate(return_next_year = replace_na(return_next_year, "No Response"))

return_next_year_df
return_next_year n
Maybe 6
Yes 71
No Response 6
Code 
ggplot(return_next_year_df, aes(x = return_next_year, y = n)) +
  geom_col()

We can see that over 70 students want to return to the camp next year.

AMINGA 2025 Student Survey Analysis By Gender

Classes

Code 
temp_class_gender <- survey_df %>% 
  dplyr::select(art, english, computer, human_rights, career_readiness, team_building,   yoga, gender) %>% 
  drop_na()

likert_subjects_gender <- likert(
  items = temp_class_gender %>% dplyr::select(art, english, computer, human_rights, career_readiness, team_building,   yoga) , 
  grouping = temp_class_gender %>% dplyr::pull(gender)
)
Code 
likert_subjects_gender$results
Group Item Not Great Meh Neutral Great Amazing
Female art 3.125000 0.000000 15.62500 6.250000 75.00000
Female english 6.250000 6.250000 37.50000 12.500000 37.50000
Female computer 0.000000 0.000000 40.62500 9.375000 50.00000
Female human_rights 0.000000 0.000000 25.00000 15.625000 59.37500
Female career_readiness 9.375000 6.250000 31.25000 18.750000 34.37500
Female team_building 3.125000 0.000000 3.12500 0.000000 93.75000
Female yoga 6.250000 9.375000 6.25000 9.375000 68.75000
Male art 0.000000 0.000000 11.11111 5.555556 83.33333
Male english 0.000000 0.000000 27.77778 13.888889 58.33333
Male computer 5.555556 0.000000 16.66667 8.333333 69.44444
Male human_rights 0.000000 0.000000 22.22222 8.333333 69.44444
Male career_readiness 2.777778 5.555556 27.77778 16.666667 47.22222
Male team_building 0.000000 0.000000 0.00000 0.000000 100.00000
Male yoga 0.000000 2.777778 11.11111 11.111111 75.00000
Code 
plot(likert_subjects_gender)

Food

Code 
temp_food_gender <- survey_df %>% dplyr::select(gender, food) %>% drop_na()

likert_food_gender <- likert(temp_food_gender %>% select(food), grouping = temp_food_gender %>% pull(gender))
likert_food_gender$results
Group Item Not Great Meh Neutral Great Amazing
Female food 0 0 8.823529 2.941177 88.23529
Male food 0 0 4.761905 4.761905 90.47619
Code 
summary(likert_food_gender, center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Female food 0 8.823529 91.17647 4.794118 0.5918339
Male food 0 4.761905 95.23810 4.857143 0.4722251
Code 
plot(likert_food_gender , centered=TRUE, center=3, include.center=TRUE)

Camp

Code 
temp_camp_gender <- survey_df %>% dplyr::select(gender, staff_overall, camp_overall) %>% drop_na()

likert_camp_gender <- likert(temp_camp_gender %>% select(staff_overall, camp_overall), grouping = temp_camp_gender %>% pull(gender))
likert_camp_gender$results
Group Item Not Great Meh Neutral Great Amazing
Female staff_overall 0 0 2.857143 5.714286 91.42857
Female camp_overall 0 0 0.000000 17.142857 82.85714
Male staff_overall 0 0 4.761905 9.523810 85.71429
Male camp_overall 0 0 0.000000 4.761905 95.23810
Code 
summary(likert_camp_gender, center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Female staff_overall 0 2.857143 97.14286 4.885714 0.4037638
Female camp_overall 0 0.000000 100.00000 4.828571 0.3823853
Male staff_overall 0 4.761905 95.23810 4.809524 0.5054867
Male camp_overall 0 0.000000 100.00000 4.952381 0.2155403
Code 
plot(likert_camp_gender, centered=TRUE, center=3, include.center=TRUE)

Sports Groups

Classes

Code 
temp_class_sport <- survey_df %>% 
  dplyr::select(art, english, computer, human_rights, career_readiness, team_building,   yoga, sport) %>% 
  drop_na()

likert_subjects_sport <- likert(
  items = temp_class_gender %>% dplyr::select(art, english, computer, human_rights, career_readiness, team_building,   yoga) , 
  grouping = temp_class_sport %>% dplyr::pull(sport)
)

likert_subjects_sport$results
Group Item Not Great Meh Neutral Great Amazing
Basketball art 0.000000 0 5.555556 16.66667 77.77778
Basketball english 0.000000 0 33.333333 22.22222 44.44444
Basketball computer 5.555556 0 16.666667 16.66667 61.11111
Basketball human_rights 0.000000 0 22.222222 16.66667 61.11111
Basketball career_readiness 0.000000 0 22.222222 22.22222 55.55556
Basketball team_building 0.000000 0 0.000000 0.00000 100.00000
Basketball yoga 0.000000 0 11.111111 11.11111 77.77778
Handball art 0.000000 0 16.000000 0.00000 84.00000
Handball english 0.000000 8 40.000000 8.00000 44.00000
Handball computer 0.000000 0 40.000000 4.00000 56.00000
Handball human_rights 0.000000 0 32.000000 8.00000 60.00000
Handball career_readiness 8.000000 4 36.000000 16.00000 36.00000
Handball team_building 4.000000 0 4.000000 0.00000 92.00000
Handball yoga 4.000000 4 8.000000 4.00000 80.00000
Volleyball art 4.000000 0 16.000000 4.00000 76.00000
Volleyball english 8.000000 0 24.000000 12.00000 56.00000
Volleyball computer 4.000000 0 24.000000 8.00000 64.00000
Volleyball human_rights 0.000000 0 16.000000 12.00000 72.00000
Volleyball career_readiness 8.000000 12 28.000000 16.00000 36.00000
Volleyball team_building 0.000000 0 0.000000 0.00000 100.00000
Volleyball yoga 4.000000 12 8.000000 16.00000 60.00000
Code 
summary(likert_subjects_sport,center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Basketball art 0.000000 5.555556 94.44444 4.722222 0.5745131
Basketball english 0.000000 33.333333 66.66667 4.111111 0.9002541
Basketball computer 5.555556 16.666667 77.77778 4.277778 1.1274936
Basketball human_rights 0.000000 22.222222 77.77778 4.388889 0.8498366
Basketball career_readiness 0.000000 22.222222 77.77778 4.333333 0.8401681
Basketball team_building 0.000000 0.000000 100.00000 5.000000 0.0000000
Basketball yoga 0.000000 11.111111 88.88889 4.666667 0.6859943
Handball art 0.000000 16.000000 84.00000 4.680000 0.7483315
Handball english 8.000000 40.000000 52.00000 3.880000 1.0923980
Handball computer 0.000000 40.000000 60.00000 4.160000 0.9865766
Handball human_rights 0.000000 32.000000 68.00000 4.280000 0.9363048
Handball career_readiness 12.000000 36.000000 52.00000 3.680000 1.2489996
Handball team_building 4.000000 4.000000 92.00000 4.760000 0.8793937
Handball yoga 8.000000 8.000000 84.00000 4.520000 1.0847427
Volleyball art 4.000000 16.000000 80.00000 4.480000 1.0456258
Volleyball english 8.000000 24.000000 68.00000 4.080000 1.2556539
Volleyball computer 4.000000 24.000000 72.00000 4.280000 1.1000000
Volleyball human_rights 0.000000 16.000000 84.00000 4.560000 0.7681146
Volleyball career_readiness 20.000000 28.000000 52.00000 3.600000 1.3228757
Volleyball team_building 0.000000 0.000000 100.00000 5.000000 0.0000000
Volleyball yoga 16.000000 8.000000 76.00000 4.160000 1.2476645
Code 
plot(likert_subjects_sport, centered=TRUE, center=3, include.center=TRUE)

Food

Code 
temp_food_sport <- survey_df %>% dplyr::select(sport, food) %>% drop_na()

likert_food_sport <- likert(temp_food_sport %>% select(food), grouping = temp_food_sport %>% pull(sport))
likert_food_sport$results
Group Item Not Great Meh Neutral Great Amazing
Basketball food 0 0 4.00000 4.000000 92.00000
Handball food 0 0 4.00000 0.000000 96.00000
Volleyball food 0 0 11.53846 7.692308 80.76923
Code 
summary(likert_food_sport, center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Basketball food 0 4.00000 96.00000 4.880000 0.4396969
Handball food 0 4.00000 96.00000 4.920000 0.4000000
Volleyball food 0 11.53846 88.46154 4.692308 0.6793662
Code 
plot(likert_food_sport , centered=TRUE, center=3, include.center=TRUE)

Camp

Code 
temp_overall_sport <- survey_df %>% dplyr::select(sport, staff_overall, camp_overall) %>% drop_na()

likert_camp_sport <- likert(temp_overall_sport %>% select(staff_overall, camp_overall), grouping = temp_overall_sport %>% pull(sport))
likert_camp_sport$results
Group Item Not Great Meh Neutral Great Amazing
Basketball staff_overall 0 0 4.000000 12.000000 84.00000
Basketball camp_overall 0 0 0.000000 0.000000 100.00000
Handball staff_overall 0 0 4.000000 8.000000 88.00000
Handball camp_overall 0 0 0.000000 12.000000 88.00000
Volleyball staff_overall 0 0 3.703704 3.703704 92.59259
Volleyball camp_overall 0 0 0.000000 18.518518 81.48148
Code 
summary(likert_camp_sport, center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Basketball staff_overall 0 4.000000 96.0000 4.800000 0.5000000
Basketball camp_overall 0 0.000000 100.0000 5.000000 0.0000000
Handball staff_overall 0 4.000000 96.0000 4.840000 0.4725816
Handball camp_overall 0 0.000000 100.0000 4.880000 0.3316625
Volleyball staff_overall 0 3.703704 96.2963 4.888889 0.4236593
Volleyball camp_overall 0 0.000000 100.0000 4.814815 0.3958474
Code 
plot(likert_camp_sport, centered=TRUE, center=3, include.center=TRUE)

Sports By Gender

Classes

Code 
temp_class_sport_gender<- survey_df %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, art, english, computer, human_rights, career_readiness, team_building,   yoga) %>% drop_na()

likert_subjects_sport_gender <- likert(
  items = temp_class_sport_gender %>% dplyr::select(english, art, computer, team_building, yoga) , 
  grouping = temp_class_sport_gender %>% dplyr::pull(`sport group`)
)

likert_subjects_sport_gender$results
Group Item Not Great Meh Neutral Great Amazing
Female Basketball english 0.000000 0.000000 33.333333 50.000000 16.66667
Female Basketball art 0.000000 0.000000 0.000000 33.333333 66.66667
Female Basketball computer 0.000000 0.000000 33.333333 0.000000 66.66667
Female Basketball team_building 0.000000 0.000000 0.000000 0.000000 100.00000
Female Basketball yoga 0.000000 0.000000 0.000000 16.666667 83.33333
Female Handball english 0.000000 15.384615 46.153846 0.000000 38.46154
Female Handball art 0.000000 0.000000 23.076923 0.000000 76.92308
Female Handball computer 0.000000 0.000000 53.846154 7.692308 38.46154
Female Handball team_building 7.692308 0.000000 7.692308 0.000000 84.61538
Female Handball yoga 7.692308 7.692308 7.692308 7.692308 69.23077
Female Volleyball english 15.384615 0.000000 30.769231 7.692308 46.15385
Female Volleyball art 7.692308 0.000000 15.384615 0.000000 76.92308
Female Volleyball computer 0.000000 0.000000 30.769231 15.384615 53.84615
Female Volleyball team_building 0.000000 0.000000 0.000000 0.000000 100.00000
Female Volleyball yoga 7.692308 15.384615 7.692308 7.692308 61.53846
Male Basketball english 0.000000 0.000000 33.333333 8.333333 58.33333
Male Basketball art 0.000000 0.000000 8.333333 8.333333 83.33333
Male Basketball computer 8.333333 0.000000 8.333333 25.000000 58.33333
Male Basketball team_building 0.000000 0.000000 0.000000 0.000000 100.00000
Male Basketball yoga 0.000000 0.000000 16.666667 8.333333 75.00000
Male Handball english 0.000000 0.000000 33.333333 16.666667 50.00000
Male Handball art 0.000000 0.000000 8.333333 0.000000 91.66667
Male Handball computer 0.000000 0.000000 25.000000 0.000000 75.00000
Male Handball team_building 0.000000 0.000000 0.000000 0.000000 100.00000
Male Handball yoga 0.000000 0.000000 8.333333 0.000000 91.66667
Male Volleyball english 0.000000 0.000000 16.666667 16.666667 66.66667
Male Volleyball art 0.000000 0.000000 16.666667 8.333333 75.00000
Male Volleyball computer 8.333333 0.000000 16.666667 0.000000 75.00000
Male Volleyball team_building 0.000000 0.000000 0.000000 0.000000 100.00000
Male Volleyball yoga 0.000000 8.333333 8.333333 25.000000 58.33333
Code 
summary(likert_subjects_sport_gender,center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Female Basketball english 0.000000 33.333333 66.66667 3.833333 0.7527727
Female Basketball art 0.000000 0.000000 100.00000 4.666667 0.5163978
Female Basketball computer 0.000000 33.333333 66.66667 4.333333 1.0327956
Female Basketball team_building 0.000000 0.000000 100.00000 5.000000 0.0000000
Female Basketball yoga 0.000000 0.000000 100.00000 4.833333 0.4082483
Female Handball english 15.384615 46.153846 38.46154 3.615385 1.1929279
Female Handball art 0.000000 23.076923 76.92308 4.538462 0.8770580
Female Handball computer 0.000000 53.846154 46.15385 3.846154 0.9870962
Female Handball team_building 7.692308 7.692308 84.61538 4.538462 1.1982894
Female Handball yoga 15.384615 7.692308 76.92308 4.230769 1.3634421
Female Volleyball english 15.384615 30.769231 53.84615 3.692308 1.4935760
Female Volleyball art 7.692308 15.384615 76.92308 4.384615 1.2608503
Female Volleyball computer 0.000000 30.769231 69.23077 4.230769 0.9268087
Female Volleyball team_building 0.000000 0.000000 100.00000 5.000000 0.0000000
Female Volleyball yoga 23.076923 7.692308 69.23077 4.000000 1.4719601
Male Basketball english 0.000000 33.333333 66.66667 4.250000 0.9653073
Male Basketball art 0.000000 8.333333 91.66667 4.750000 0.6215816
Male Basketball computer 8.333333 8.333333 83.33333 4.250000 1.2154311
Male Basketball team_building 0.000000 0.000000 100.00000 5.000000 0.0000000
Male Basketball yoga 0.000000 16.666667 83.33333 4.583333 0.7929615
Male Handball english 0.000000 33.333333 66.66667 4.166667 0.9374369
Male Handball art 0.000000 8.333333 91.66667 4.833333 0.5773503
Male Handball computer 0.000000 25.000000 75.00000 4.500000 0.9045340
Male Handball team_building 0.000000 0.000000 100.00000 5.000000 0.0000000
Male Handball yoga 0.000000 8.333333 91.66667 4.833333 0.5773503
Male Volleyball english 0.000000 16.666667 83.33333 4.500000 0.7977240
Male Volleyball art 0.000000 16.666667 83.33333 4.583333 0.7929615
Male Volleyball computer 8.333333 16.666667 75.00000 4.333333 1.3026779
Male Volleyball team_building 0.000000 0.000000 100.00000 5.000000 0.0000000
Male Volleyball yoga 8.333333 8.333333 83.33333 4.333333 0.9847319
Code 
plot(likert_subjects_sport_gender, centered=TRUE, center=3, include.center=TRUE)

Food

Code 
temp_food_sport_gender <- survey_df  %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, food) %>% drop_na()

likert_food_sport_gender <- likert(temp_food_sport_gender %>% select(food), grouping = temp_food_sport_gender %>% pull(`sport group`))
likert_food_sport_gender$results
Group Item Not Great Meh Neutral Great Amazing
Female Basketball food 0 0 0.000000 0.000000 100.00000
Female Handball food 0 0 0.000000 0.000000 100.00000
Female Volleyball food 0 0 23.076923 7.692308 69.23077
Male Basketball food 0 0 6.250000 6.250000 87.50000
Male Handball food 0 0 7.692308 0.000000 92.30769
Male Volleyball food 0 0 0.000000 7.692308 92.30769
Code 
summary(likert_food_sport_gender, center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Female Basketball food 0 0.000000 100.00000 5.000000 0.0000000
Female Handball food 0 0.000000 100.00000 5.000000 0.0000000
Female Volleyball food 0 23.076923 76.92308 4.461538 0.8770580
Male Basketball food 0 6.250000 93.75000 4.812500 0.5439056
Male Handball food 0 7.692308 92.30769 4.846154 0.5547002
Male Volleyball food 0 0.000000 100.00000 4.923077 0.2773501
Code 
plot(likert_food_sport_gender , centered=TRUE, center=3, include.center=TRUE)

Camp

Code 
temp_overall_sport_gender <- survey_df %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, staff_overall, camp_overall) %>% drop_na()

likert_camp_sport_gender <- likert(temp_overall_sport_gender %>% select(staff_overall, camp_overall), grouping = temp_overall_sport_gender %>% pull(`sport group`))
likert_camp_sport_gender$results
Group Item Not Great Meh Neutral Great Amazing
Female Basketball staff_overall 0 0 0.000000 11.111111 88.88889
Female Basketball camp_overall 0 0 0.000000 0.000000 100.00000
Female Handball staff_overall 0 0 8.333333 8.333333 83.33333
Female Handball camp_overall 0 0 0.000000 16.666667 83.33333
Female Volleyball staff_overall 0 0 0.000000 0.000000 100.00000
Female Volleyball camp_overall 0 0 0.000000 28.571429 71.42857
Male Basketball staff_overall 0 0 6.250000 12.500000 81.25000
Male Basketball camp_overall 0 0 0.000000 0.000000 100.00000
Male Handball staff_overall 0 0 0.000000 7.692308 92.30769
Male Handball camp_overall 0 0 0.000000 7.692308 92.30769
Male Volleyball staff_overall 0 0 7.692308 7.692308 84.61538
Male Volleyball camp_overall 0 0 0.000000 7.692308 92.30769
Code 
summary(likert_camp_sport_gender, center = 3, ordered = TRUE)
Group Item low neutral high mean sd
Female Basketball staff_overall 0 0.000000 100.00000 4.888889 0.3333333
Female Basketball camp_overall 0 0.000000 100.00000 5.000000 0.0000000
Female Handball staff_overall 0 8.333333 91.66667 4.750000 0.6215816
Female Handball camp_overall 0 0.000000 100.00000 4.833333 0.3892495
Female Volleyball staff_overall 0 0.000000 100.00000 5.000000 0.0000000
Female Volleyball camp_overall 0 0.000000 100.00000 4.714286 0.4688072
Male Basketball staff_overall 0 6.250000 93.75000 4.750000 0.5773503
Male Basketball camp_overall 0 0.000000 100.00000 5.000000 0.0000000
Male Handball staff_overall 0 0.000000 100.00000 4.923077 0.2773501
Male Handball camp_overall 0 0.000000 100.00000 4.923077 0.2773501
Male Volleyball staff_overall 0 7.692308 92.30769 4.769231 0.5991447
Male Volleyball camp_overall 0 0.000000 100.00000 4.923077 0.2773501
Code 
plot(likert_camp_sport_gender, centered=TRUE, center=3, include.center=TRUE)

Comments

I loved about the sport

Code 
sport_love_count <- prep_word_cloud(i_loved_about_the_sport_eu_amei_sobre_a_modalidade) 
## Joining, by = "word"

wordcloud(words = sport_love_count$word, 
          freq = sport_love_count$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

I would change about the sport

Code 
sport_change_count <- prep_word_cloud(i_would_change_about_the_sport_eu_mudaria_sobre_a_modalidade) 
## Joining, by = "word"

wordcloud(words = sport_change_count$word, 
          freq = sport_change_count$n, 
          min.freq = 1, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Arts Comments

Code 
arts_comments <- prep_word_cloud(comments_regarding_art_class_comentarios_sobre_as_aulas_de_arte) 
## Joining, by = "word"

wordcloud(words = arts_comments$word, 
          freq = arts_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

English Comments

Code 
english_comments <- prep_word_cloud(comments_regarding_english_class_comentarios_sobre_a_aula_de_ingles) 
## Joining, by = "word"

wordcloud(words = english_comments$word, 
          freq = english_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Computer Comments

Code 
computer_comments <- prep_word_cloud(comments_regarding_computer_class_comentarios_sobre_as_aulas_de_informatica) 
## Joining, by = "word"

wordcloud(words = computer_comments$word, 
          freq = computer_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Human Rights

Code 
rights_comments <- prep_word_cloud(comments_regarding_human_rights_comentarios_sobre_direitos_humanos) 
## Joining, by = "word"

wordcloud(words = rights_comments$word, 
          freq = rights_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Team Building

Code 
team_building_comments <- prep_word_cloud(comments_regarding_team_building_comentarios_sobre_trabalho_de_equipa) 
## Joining, by = "word"

wordcloud(words = team_building_comments$word, 
          freq = team_building_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Yoga Comments

Code 
yoga_comments <- prep_word_cloud(comments_regarding_yoga_class_comentarios_sobre_a_aula_de_yoga) 
## Joining, by = "word"

wordcloud(words = yoga_comments$word, 
          freq = yoga_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Food Comments

Code 
food_comments <- prep_word_cloud(comments_regarding_the_food_comentarios_sobre_a_comida) 
## Joining, by = "word"

wordcloud(words = food_comments$word, 
          freq = food_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

AMINGA Staff Comments

Code 
staff_comments <- prep_word_cloud(aminga_staff_2024_overall_insert_appreciations_for_any_of_the_staff_equipa_de_aminga_em_geral_algum_comentario_sobre_qualquer_um_da_equipa_aminga) 
## Joining, by = "word"

wordcloud(words = staff_comments$word, 
          freq = staff_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Any Comments or Suggestions

Code 
any_comments <- prep_word_cloud(any_comments_or_suggestions_algum_comentario_ou_sugestao) 
## Joining, by = "word"

wordcloud(words = any_comments$word, 
          freq = any_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))